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India’s newest nuclear reactor connects to grid
Unit 4 at Kakrapar nuclear power plant was connected to the grid on February 20, the Nuclear Power Corporation of India Ltd. (NPCIL) has announced. The 700-MWe pressurized heavy water reactor achieved first criticality on December 17, 2023.
Lei Jin, Kaushik Banerjee
Nuclear Science and Engineering | Volume 194 | Number 3 | March 2020 | Pages 190-206
Technical Paper | doi.org/10.1080/00295639.2019.1678104
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) simulation is used to solve the eigenvalue form of the Boltzmann transport equation to estimate various parameters such as fuel pin flux distributions that are crucial for the safe and efficient operation of nuclear systems (e.g., a nuclear reactor). Monte Carlo eigenvalue simulation uses a sample mean over many stationary cycles (iterations) to estimate various parameters important to nuclear systems. A variance estimate of the sample mean is often used for calculating the confidence intervals. However, MC eigenvalue simulation variance estimators that ignore the intercycle correlation underestimate the true variance of the estimated quantity. This paper presents novel data-adaptive approaches based on a simple autoregressive (AR) model and sigmoid functions to improve MC variance estimation. The standard MC sample-based variance estimator (or naïve estimator) and the spectral density–based MC variance estimator are enhanced by adding data-adaptive components that reduce their bias and improve performance. By investigating the frequency pattern of the AR(1) (order 1) model, two adaptive spectral estimators and one adaptive naïve estimator are proposed. The proposed estimators manifest superior performance when applied to three test problems compared to the standard spectral density–based estimator previously introduced by the authors. These new estimators are straightforward, as they use online algorithms and do not require storage of tallies from all active cycles.